class_docstr: A pretrained model implementation for Grapheme-to-Phoneme (G2P) models
  that take raw natural language text as an input and
  
  Example
  -------
  >>> text = ("English is tough. It can be understood "
  ...         "through thorough thought though")
  >>> from speechbrain.inference.text import GraphemeToPhoneme
  >>> tmpdir = getfixture('tmpdir')
  >>> g2p = GraphemeToPhoneme.from_hparams('path/to/model', savedir=tmpdir) # doctest: +SKIP
  >>> phonemes = g2p.g2p(text) # doctest: +SKIP
docstr: A convenience callable wrapper - same as G2P
  
  Arguments
  ---------
  text: str or list[str]
      a single string to be encoded to phonemes - or a
      sequence of strings
  
  Returns
  -------
  result: list
      if a single example was provided, the return value is a
      single list of phonemes
docstr_tok: ["A","convenience","callable","wrapper","-","same","as","G2P","Arguments","--","--","--","--","-","text",":","str","or","list","[","str","]","a","single","string","to","be","encoded","to","phonemes","-","or","a","sequence","of","strings","Returns","--","--","--","-","result",":","list","if","a","single","example","was","provided",",","the","return","value","is","a","single","list","of","phonemes"]
code: def __call__(self, text):
      """A convenience callable wrapper - same as G2P
  
      Arguments
      ---------
      text: str or list[str]
          a single string to be encoded to phonemes - or a
          sequence of strings
  
      Returns
      -------
      result: list
          if a single example was provided, the return value is a
          single list of phonemes
      """
      return self.g2p(text)
  
imports: import torch
  from itertools import chain
  from speechbrain.inference.interfaces import Pretrained, EncodeDecodePipelineMixin
label_desc: Use image node_id 7 for calling the GraphemeToPhoneme obj's underlying member method code with example usage: obj.__call__(text) and returns: self